{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Feature.xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Features.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_chousui</th>\n",
       "      <th>SVAI_kaihua</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509428</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456349</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466787</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479437</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438036</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537977</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511967</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380461</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  SVAI_chousui  SVAI_kaihua   \n",
       "0                            290.517675  ...      0.509428     0.502203  \\\n",
       "1                            290.784441  ...      0.456349     0.444113   \n",
       "2                            290.271875  ...      0.375751     0.406768   \n",
       "3                            290.136585  ...      0.466787     0.392344   \n",
       "4                            291.813767  ...      0.479437     0.456340   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.438036     0.427608   \n",
       "98                           292.107970  ...      0.537977     0.518776   \n",
       "99                           290.932713  ...      0.511967     0.563379   \n",
       "100                          290.356689  ...      0.480808     0.460888   \n",
       "101                          291.427115  ...      0.380461     0.390789   \n",
       "\n",
       "     WDRVI_bajie  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0      -0.270294       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1      -0.220879       0.020097    0.272293     0.321097      0.313054   \n",
       "2      -0.273682      -0.176015    0.255247     0.291260      0.276982   \n",
       "3      -0.305916       0.009030    0.277992     0.313744      0.335973   \n",
       "4      -0.155182       0.027083    0.329961     0.356682      0.342203   \n",
       "..           ...            ...         ...          ...           ...   \n",
       "97     -0.222630      -0.033628    0.276777     0.307460      0.306785   \n",
       "98     -0.049587       0.143995    0.356591     0.372768      0.380640   \n",
       "99      0.082507       0.149694    0.389395     0.347135      0.345173   \n",
       "100    -0.102704       0.005744    0.309025     0.317261      0.348503   \n",
       "101    -0.356993      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.062063  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo'])\n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#d=d.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])\n",
    "#dd=dd.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bo-xgboost</th>\n",
       "      <td>0.09374</td>\n",
       "      <td>541.292513</td>\n",
       "      <td>704.155736</td>\n",
       "      <td>0.724801</td>\n",
       "      <td>0.729479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）      解释方差\n",
       "bo-xgboost       0.09374   541.292513   704.155736   0.724801  0.729479"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>7303.940918</td>\n",
       "      <td>-2.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>7278.649414</td>\n",
       "      <td>21.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>5490.393555</td>\n",
       "      <td>-3.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7964.764648</td>\n",
       "      <td>0.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>6903.407715</td>\n",
       "      <td>-9.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>7560.453613</td>\n",
       "      <td>14.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>7037.033691</td>\n",
       "      <td>-7.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>6803.296387</td>\n",
       "      <td>18.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>6529.838379</td>\n",
       "      <td>19.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>6305.773926</td>\n",
       "      <td>-4.99%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred precent\n",
       "0    7467.000138  7303.940918  -2.18%\n",
       "1    5972.666001  7278.649414  21.87%\n",
       "2    5687.791498  5490.393555  -3.47%\n",
       "3    7959.597222  7964.764648   0.06%\n",
       "4    7662.496889  6903.407715  -9.91%\n",
       "..           ...          ...     ...\n",
       "97   6613.422500  7560.453613  14.32%\n",
       "98   7579.469809  7037.033691  -7.16%\n",
       "99   5754.140236  6803.296387  18.23%\n",
       "100  5467.128359  6529.838379  19.44%\n",
       "101  6637.200003  6305.773926  -4.99%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (Bo-XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(1000, 10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
